用神经扩散-对流-反应方程推进意见动力学建模
Advancing Opinion Dynamics Modeling with Neural Diffusion-Convection-Reaction Equation
AI总结：
本文提出OPINN框架，通过整合物理信息与神经网络，提升意见动力学建模的预测性能和物理可解释性。
摘要：
先进的意见动力学建模对于解析社会行为至关重要，强调其在缓解极化和保障网络空间中的作用。为协同机制可解释性与数据驱动的灵活性，近期研究探索了将物理信息神经网络（PINNs）整合到意见建模中的方法。尽管有前景，现有方法局限于不完整的先验知识，缺乏一个综合的物理系统来整合局部、全局和内生层面的动力学。此外，现有方法中采用的惩罚约束难以深入编码物理先验，导致优化病理和潜在表示与物理透明度之间的差异。为此，我们通过受相互作用粒子理论启发的扩散-对流-反应（DCR）系统，从物理角度解释意见动力学。基于神经ODEs，我们定义了神经意见动力学，协调神经网络与物理先验，并进一步提出了OPINN，一种用于意见动力学建模的物理信息神经框架。在真实世界和合成数据集上评估，OPINN在意见演化预测中实现了最先进的性能，为网络、物理和社会系统交汇提供了有前景的范式。
英文摘要：
Advanced opinion dynamics modeling is vital for deciphering social behavior, emphasizing its role in mitigating polarization and securing cyberspace. To synergize mechanistic interpretability with data-driven flexibility, recent studies have explored the integration of Physics-Informed Neural Networks (PINNs) for opinion modeling. Despite this promise, existing methods are tailored to incomplete priors, lacking a comprehensive physical system to integrate dynamics from local, global, and endogenous levels. Moreover, penalty-based constraints adopted in existing methods struggle to deeply encode physical priors, leading to optimization pathologies and discrepancy between latent representations and physical transparency. To this end, we offer a physical view to interpret opinion dynamics via Diffusion-Convection-Reaction (DCR) system inspired by interacting particle theory. Building upon the Neural ODEs, we define the neural opinion dynamics to coordinate neural networks with physical priors, and further present the OPINN, a physics-informed neural framework for opinion dynamics modeling. Evaluated on real-world and synthetic datasets, OPINN achieves state-of-the-art performance in opinion evolution forecasting, offering a promising paradigm for the nexus of cyber, physical, and social systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
